Be it the everyday life in your home or outside, you knowingly or unknowingly engage with programming. The modern world involves daily tasks that you perform while driving or in a grocery store, scanning the bar codes, or even sending an email at the workplace. All of these tasks and many others involve programming languages, working to the best of their deliverance. With the world going digital with processes, at home, and work as well, everything is propagated via machines and codes. The cruise control and navigational systems in your cars, the traffic lights network, and the codes in the elevators take you to the desired floor; all of these are computer controlled.

This clearly shows that even if you are not a developer, you are involved in running your day based on computer-guided, code-centric actions, at home and work alike. Humans and machines process information differently, as we all know. Thus, programming languages are the key to bridging the gap between people and computers. These programming languages are the reason we can enjoy an intelligently mechanized world around us. This certainly pushes us to answer a question;

Why is everyone trying to get in on the application development scene?”

Just because these programming languages set a career path that offers astonishingly higher remuneration and a data science career that is built upon a strong programming foundation stands the longest test of time in the data science world. As the data becomes humungous and grows beyond the reach of human management, it becomes inevitable to bring in some clever programming strategies to leverage the highest benefits for the organizations involved.

According to a report generated by IBM, Data science jobs would likely grow by 30%. Another report from the US Bureau of Labor Statistics states that about 11.5 million jobs will be created by the year 2026. This stresses the fact of making learning programming languages a bare necessity for all data science professionals to offer the best of their skills. This also makes it critical to pick the right programming language, that provides a flying start to your data science career in the long run.

A programming language is nothing but how programmers and developers interact with machines/ computers. These involve using intelligently-formulated programming languages suited to a particular task at hand. Listed below are 6 most popular programming languages that are doing the rounds this year and are all set to create waves for future programming needs as well:


Created in the year 1995, JavaScript tops the rank for being world’s most popular programming language. It helps in creating dynamic and interactive web content and is used widely used by websites.


You must have heard the name a gazillion times- Python is a general-purpose programming language for beginners and easy to comprehend. It is used to build 2D-3D imaging and animation packages and famous video games. Python applications are a highly critical language for tutorials and documentation, and its coding skills are widely used in technical education as well as the business arena.

  • JAVA

Owned by Oracle, Java is a widely used computing platform and a famous programming language. It offers a great start to your learning curve as a programmer and is widely used on major websites of today- Google, Amazon, Twitter, YouTube, and others. Our desktops, servers, mobile phones, tablets, Blu-ray players, televisions, and web browsers are all running on Java-based applications.

  • R

First released in 1993, R is an open-source, interpreted programming language. It commands huge demand in the data science industry for its statistical computing applications. It lends great support for managing data and offers a huge variety of packages. It is apt for machine learning projects and analytics.

  • PHP (Hypertext Preprocessor)

Considered a relatively easier language to learn, it can be used to write command-line scripts and develop high-end desktop applications. The most dynamic and interactive web [ages that you come across on the web are all developed by using R. This makes it the most popular language, being deployed by over 80% of internet websites including big names such as Facebook, Yahoo, etc.

  • C and C++

The oldest and root of other programming languages such as C#, Java, Javascript, and C++; and C is the preliminary version of C++. C++ allows much higher control over other languages, whereas C helps beginners to get their roots perfected in programming.

While there are many programming languages, making it to the list of top programming languages takes performance and long-time industry acceptance. There are many data science certifications as well, that offer a package inclusive of such incredible and competitive programming languages like Python and R, that are most wanted by employers in the data science industry. Investing in the most-trusted data science certification is the key to a flourishing data science career ahead. Bring in the great future by making an informed decision today.

Why Python is the Best Language for Data Science

Regarding data science, Python is the most popular language professionals use. Its popularity stems from its simplicity, flexibility, and extensive libraries catering to various data science tasks. Python is a high-level, object-oriented programming language that is easy to learn and understand. Its clean syntax allows data scientists to write clean and concise code, making it a language of choice for data analysis and visualization.

One of the major advantages of Python is its vast collection of open-source libraries, such as NumPy, Pandas, and Matplotlib. These libraries are designed to perform mathematical operations, manipulate data, and generate visualizations, making data analysis more efficient and productive. Python’s libraries also make it easy to integrate with other tools commonly used in data science, such as SQL and Hadoop.

Another reason why Python is widely used in data science is its ability to handle large data sets efficiently. Python’s libraries, such as Dask and Apache Spark, are designed to work with distributed systems, making it easy to handle large volumes of data. Python’s multi-threading capabilities also make it ideal for parallel processing, allowing data scientists to analyze data faster and more efficiently.

In addition to its technical capabilities, Python has a strong community of developers and users who contribute to its development and support. This community ensures that Python remains up-to-date with the latest trends and technologies, making it a reliable and future-proof language for data science.

Python is the best language for data science because of its simplicity, flexibility, extensive libraries, ability to handle large data sets efficiently, and strong community support. Whether you are a beginner or an experienced data scientist, Python is a language that will help you achieve your data science goals.

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Olivia is a seasoned blogger with a flair for lifestyle and fashion. With over 6 years of experience, she shares her passion for the latest trends and styles, offering inspiration and guidance to her audience on all things lifestyle-related.